Dose Volume Histogram Prediction for Radiation Plan Optimization
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Solution Overview
Problem
Existing radiation treatment plans often fail to adequately discriminate between target volumes and adjacent tissues, leading to unsuitable dose distributions due to difficult-to-describe criteria in dose volume histogram shapes and three-dimensional distributions.
Innovation Solution
A machine learning model is trained using a plurality of radiation treatment plans to predict desired dose volume histogram shapes, incorporating confidence intervals and clinical metrics, and is used to iteratively optimize treatment plans by generating cost function terms to penalize deviations from typical distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic optimization processes are used to generate radiation treatment plans, then treatment plan generation efficiency is improved, but the precision of dose distribution discrimination between target volumes and adjacent tissues deteriorates
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the optimization process and the dose distribution evaluation. The model predicts dose volume histogram shapes and provides cost function terms that guide the optimization process, enabling automatic plan generation while maintaining precision in dose distribution discrimination through data-driven predictions rather than traditional mathematical calculations alone
Solution Approach 2:
The patent replaces traditional mechanical/mathematical optimization calculations with a machine learning-based prediction system. Instead of relying solely on mathematical modeling to predict treatment outcomes, the system uses trained neural networks to forecast dose volume histogram shapes, substituting complex mathematical computations with learned patterns from training data to achieve both efficiency and precision
2Device complexity
If traditional optimization processes are used without machine learning, then computational complexity is reduced, but the ability to predict desired dose volume histogram shapes deteriorates
Solution Approach 1:
The machine learning model is pre-trained using a comprehensive dataset of treatment plans and their corresponding dose volume histograms before actual treatment planning. This preliminary training action allows the model to capture complex relationships between treatment parameters and dose distributions, enabling accurate predictions during actual use without requiring complex real-time computations
Solution Approach 2:
The system creates a computational model that copies and learns from existing successful treatment plans and their dose volume histograms. By training on replicated data from multiple treatment cases, the model learns to predict desired dose distribution shapes, effectively copying the patterns of successful treatments to guide new plan generation without requiring complex analytical derivations
Data Source
AI summary
A control circuit accesses a plurality of information items that each correspond to a resultant dose volume histogram shape for a corresponding different radiation treatment plan. The control circuit then trains a machine learning model to predict a desired dose volume histogram shape using that plurality of information items as a training corpus.